Borrowing it
Nothing to install: this file belongs to thunderkds/easy-ui-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/thunderkds/easy-ui-mcp/main/.claude/skills/learn/SKILL.mdgit clone --depth 1 https://github.com/thunderkds/easy-ui-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/thunderkds/easy-ui-mcp/learn)<a href="https://agentmods.dev/skills/thunderkds/easy-ui-mcp/learn"><img src="https://agentmods.dev/badge/skills/thunderkds/easy-ui-mcp/learn/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/thunderkds/easy-ui-mcp/learn"><img src="https://agentmods.dev/badge/skills/thunderkds/easy-ui-mcp/learn.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00072 | $0.01719 |
| Opus 5.5 | $0.00029 | $0.00688 |
| Sonnet 5.5 | $0.00014 | $0.00344 |
| Haiku 4.5 | $0.00007 | $0.00172 |
Grade A, and why
learn scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role: Learning Record Specialist
Transform non-obvious insights from this session into durable Learning Records in memory/learning-records/, update memory/MEMORY.md, route confirmed facts to the appropriate cold file, and (conditionally) surface a skill promotion candidate.
Karpathy Operational Commands
- Ask vs. Guess: If the boundary between "materiality pass" and "noise" is unclear, flag it to the Supervisor before writing an LR. Never guess that something qualifies.
- Surgical Changes: Touch only
memory/learning-records/,memory/MEMORY.md, and the single cold file the routing table designates. Append only — never rewrite existing cold file sections. - Simplicity First: One LR per discrete insight; never merge unrelated insights.
Workflow
Step 1 — Materiality Gate
Write an LR only when at least one of these signals is present:
| Signal | Example |
|---|---|
| User corrects the Supervisor or an agent | "No, that's wrong — it should be X" |
| User discloses a preference, knowledge level, or working style | "I always prefer short PRs" |
| A non-obvious domain pattern is confirmed (demonstrated, not merely mentioned) | Third time the same edge case breaks the same assumption |
| A misconception is corrected (agent was wrong; user set it straight) | Agent claimed X; user showed Y |
| A "this was surprising" moment lands (unexpected behaviour, undocumented constraint) | "API rate-limits per org, not per user" |
Do NOT write an LR for any of the following — discard immediately:
- Greetings, acknowledgements, "ok", "thanks", small-talk
- Material merely explained but not demonstrated or confirmed
- Terms already present verbatim in
memory/glossary.md - Activity logs ("we implemented X today", "T009 is done")
- Restatements of content already in
memory/decisions.mdormemory/learnings.md
If no signals pass, announce: "No material insights detected — no LRs written." and stop.
Step 2 — Classify
Assign each insight one type: project (codebase, domain patterns, gotchas, spec clarifications, architecture) or user (personal preferences, knowledge level, working style). One insight → one type; if it straddles both, split into two records.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 129 lines · 72 tokens per session scan A 80a6b58d0f3a
learn is a skill published in the GitHub repository thunderkds/easy-ui-mcp (1 stars, last pushed today), licensed MIT. It adds 72 tokens to every session and 1,719 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-30.
Other skills, from other repositories
knowledge-ingest
Ingest documents, articles, conversations, or supplied text into Knowledge with retained originals, sourced claims, entity updates, source notes, and cross-links; also learn durable lessons from a set of conversations, transcripts, or past sessions (/learn), including the Learn from conversations and Learn from Memory…
forget
Remove a personal fact, preference, relationship detail, topic, or prior event from Muse's active memory and stop existing copies or automations from bringing it back. Use for explicit requests such as 'forget that', 'don't remember this about me', or 'remove that from your memory'. Do not use when 'forget it' merely…
mem0-oss-to-platform
Plan, then execute, a migration of a project from the mem0 open-source / self-hosted SDK (local Memory class) to the mem0 Platform / hosted SDK (MemoryClient). TRIGGER when: the developer wants to move, switch, or migrate mem0 off OSS/self-hosted to the hosted API, e.g. "migrate my mem0 setup to the platform", "switch…
Cortex
LifeOS memory, prior-session recall, and learning capture. USE WHEN capture this learning, document this learning, what did we learn recently, recent learnings, cortex, knowledge, knowledge base, search knowledge, what do we know about, archive, harvest, knowledge status, develop note, add to knowledge, add a people…
memory
Use when the user asks to remember, recall, forget, update, search, or inspect durable OpenSquilla memory, including profile facts in USER.md and long-term notes in MEMORY.md or memory//.md.
ha-data-stores
Map of Hope Agent's local data stores and safe read-only query workflow. Use when the user asks where Hope Agent stores data, wants to inspect sessions/messages/memory/logs/background jobs/knowledge indexes/settings, asks the model to query local app data, or debugging requires checking persisted state. Trigger…